Explains ISO/IEC 25024 data quality measures
Each measure is a ratio of a numerator to a denominator over a stated population at an as-of date, the denominator being the rows the characteristic applies to, so that the result can be recomputed and compared (ISO/IEC 25024:2015).
Can AI do this?
AI does this partly Some of the tested AI models master this; others do not show it. 2 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 100% | Mastered |
| gemini-3.5-flash-lite | Low cost | 99% | Not shown |
| gpt-6-luna | Low cost | 99% | Not shown |
| claude-haiku-5-5 | Low cost | 100% | Mastered |
Mastered means a probability of at least 90% that the model has this knobit, estimated from its graded answers. “Not shown” means the answers do not settle it either way. How the models are tested.
What it looks like in practice
Expected result: Each measure is a ratio of a numerator to a denominator over a stated population at an as-of date, the denominator being the rows the characteristic applies to, so that the result can be recomputed and compared (ISO/IEC 25024:2015).
How it is assessed: Answers a written question on the ISO/IEC 25024 data quality measure correctly
Related knobits
Builds on
Needed for
- Q-401 Recognises a null counted as missing where the element does not apply
- Q-404 Recognises a placeholder value counted as present
- Q-405 Recognises a quality ratio with no population, numerator or denominator
- Q-406 Recognises one quality ratio over differing populations
- Q-754 Explains like-for-like trends of quality measures
All 10 knobits of Profile and assess data quality.
Source: Practice the stage 4 reference and review checklist rest on